## # A tibble: 19 x 2
## Q29 n
## <dbl> <int>
## 1 13 915
## 2 NA 771
## 3 5 745
## 4 4 636
## 5 8 351
## 6 3 180
## 7 11 173
## 8 16 151
## 9 10 128
## 10 7 119
## 11 17 114
## 12 1 84
## 13 12 71
## 14 18 69
## 15 6 55
## 16 2 21
## 17 14 13
## 18 9 6
## 19 15 3
## # A tibble: 5 x 2
## Q32 n
## <dbl> <int>
## 1 1 1038
## 2 2 1122
## 3 3 1121
## 4 4 702
## 5 NA 622
## # A tibble: 5 x 2
## Q32 n
## <dbl> <int>
## 1 1 72
## 2 2 81
## 3 3 80
## 4 4 75
## 5 NA 463
## # A tibble: 13 x 2
## Q33 n
## <dbl> <int>
## 1 1 1004
## 2 2 63
## 3 3 143
## 4 4 904
## 5 5 60
## 6 6 68
## 7 7 66
## 8 8 252
## 9 9 419
## 10 10 420
## 11 11 224
## 12 12 388
## 13 NA 594
## # A tibble: 13 x 2
## Q33 n
## <dbl> <int>
## 1 1 947
## 2 4 835
## 3 10 399
## 4 9 382
## 5 12 351
## 6 8 237
## 7 11 199
## 8 NA 133
## 9 3 119
## 10 6 62
## 11 5 57
## 12 7 57
## 13 2 56
## # A tibble: 6 x 2
## Q37 n
## <dbl> <int>
## 1 1 2985
## 2 2 1016
## 3 3 29
## 4 4 44
## 5 1234 1
## 6 NA 530
Drop NAs for specific questions and filter out disciplines with fewer than 30 (the cutoff) students in sample
## # A tibble: 18 x 2
## Q29 n
## <dbl> <int>
## 1 13 644
## 2 5 540
## 3 4 443
## 4 8 243
## 5 16 120
## 6 3 111
## 7 11 111
## 8 7 95
## 9 10 95
## 10 17 81
## 11 1 70
## 12 12 57
## 13 18 42
## 14 6 35
## 15 2 16
## 16 14 8
## 17 9 3
## 18 15 1
## # A tibble: 14 x 2
## Q29 n
## <dbl> <int>
## 1 13 644
## 2 5 540
## 3 4 443
## 4 8 243
## 5 16 120
## 6 3 111
## 7 11 111
## 8 7 95
## 9 10 95
## 10 17 81
## 11 1 70
## 12 12 57
## 13 18 42
## 14 6 35
Major counts and percentages
| Q29 | major | n | pct_total | cumulat_pct |
|---|---|---|---|---|
| 13 | Mec | 644 | 23.97 | 23.97 |
| 5 | Che | 540 | 20.10 | 44.06 |
| 4 | Civ | 443 | 16.49 | 60.55 |
| 8 | Ele | 243 | 9.04 | 69.59 |
| 16 | Softw | 120 | 4.47 | 74.06 |
| 3 | Bio | 111 | 4.13 | 78.19 |
| 11 | Ind | 111 | 4.13 | 82.32 |
| 7 | Comp | 95 | 3.54 | 85.86 |
| 10 | Env/Eco | 95 | 3.54 | 89.39 |
| 17 | Str/Arc | 81 | 3.01 | 92.41 |
| 1 | Aer/Oce | 70 | 2.61 | 95.01 |
| 12 | Mat | 57 | 2.12 | 97.13 |
| 18 | Gen | 42 | 1.56 | 98.70 |
| 6 | Con | 35 | 1.30 | 100.00 |
Gender counts overall
| Q37 | n | pct_total |
|---|---|---|
| 1 | 1973 | 73.43 |
| 2 | 678 | 25.23 |
| 3 | 15 | 0.56 |
| 4 | 21 | 0.78 |
fill in 0s for NAs for specific items (Q1, Q3, Q5)
Drop majors with low counts (below 30 students in sample)
## # A tibble: 14 x 2
## Q29 n
## <dbl> <int>
## 1 13 618
## 2 5 523
## 3 4 433
## 4 8 240
## 5 16 118
## 6 3 110
## 7 11 108
## 8 7 93
## 9 10 90
## 10 17 78
## 11 1 70
## 12 12 55
## 13 18 40
## 14 6 32
## # A tibble: 14 x 2
## major n
## <chr> <int>
## 1 Mec 618
## 2 Che 523
## 3 Civ 433
## 4 Ele 240
## 5 Softw 118
## 6 Bio 110
## 7 Ind 108
## 8 Comp 93
## 9 Env/Eco 90
## 10 Str/Arc 78
## 11 Aer/Oce 70
## 12 Mat 55
## 13 Gen 40
## 14 Con 32
Drop majors with NA as major
## # A tibble: 14 x 2
## major n
## <chr> <int>
## 1 Mec 618
## 2 Che 523
## 3 Civ 433
## 4 Ele 240
## 5 Softw 118
## 6 Bio 110
## 7 Ind 108
## 8 Comp 93
## 9 Env/Eco 90
## 10 Str/Arc 78
## 11 Aer/Oce 70
## 12 Mat 55
## 13 Gen 40
## 14 Con 32
First perform dimension reduction using UMAP
## NULL
## [,1] [,2]
## [1,] 15.62332 -15.97910
## [2,] 11.12283 -18.44035
## [3,] -34.20802 18.18004
## [1] 15.623323 11.122833 -34.208024 -34.419584 6.551877 -34.582958
## [1] -15.97910 -18.44035 18.18004 18.60377 -23.96864 18.41222
Next, perform clustering with HDBSCAN
## HDBSCAN clustering for 2608 objects.
## Parameters: minPts = 120
## The clustering contains 6 cluster(s) and 241 noise points.
##
## 0 1 2 3 4 5 6
## 241 1080 575 175 147 264 126
##
## Available fields: cluster, minPts, cluster_scores, membership_prob,
## outlier_scores, hc
Join the dataframes back together again
## # A tibble: 7 x 2
## cluster n
## <dbl> <int>
## 1 1 1080
## 2 2 575
## 3 5 264
## 4 0 241
## 5 3 175
## 6 4 147
## 7 6 126
## # A tibble: 7 x 3
## cluster_time_rank cluster cluster_avg
## <int> <dbl> <dbl>
## 1 1 1 0.386
## 2 2 4 3.46
## 3 3 0 4.76
## 4 4 5 5.14
## 5 5 6 7.13
## 6 6 2 9.24
## 7 7 3 17.4
Set clustering colors for all plots
Same information but faceted with clusters
** This is a good plot for seeing that a cluster’s beliefs about effects of global warming on different populations at different times vary in a clear pattern
| coll_exp_item | coll_exp_item_name | statistic | p.value | parameter | method |
|---|---|---|---|---|---|
| Q6a | Extracurr: Eng. research | 10.2672493 | 0.0361587 | 4 | Kruskal-Wallis rank sum test |
| Q6d | Extracurr: Work/volunteer in dev. cou. | 11.4765919 | 0.0216990 | 4 | Kruskal-Wallis rank sum test |
| Q6e | Extracurr: Work for eng. co. | 12.7560859 | 0.0125312 | 4 | Kruskal-Wallis rank sum test |
| Q6i | Extracurr: Travel w/ int. ser. gr. | 1.1376456 | 0.8882542 | 4 | Kruskal-Wallis rank sum test |
| Q6j | Extracurr: Part of env. sus. gr. | 17.7670286 | 0.0013704 | 4 | Kruskal-Wallis rank sum test |
| Q7a_tally | Course top: Energy supply | 3.3153187 | 0.5065088 | 4 | Kruskal-Wallis rank sum test |
| Q7b_tally | Course top: Energy demand | 1.9897052 | 0.7376525 | 4 | Kruskal-Wallis rank sum test |
| Q7c_tally | Course top: Climate change | 8.9512570 | 0.0623294 | 4 | Kruskal-Wallis rank sum test |
| Q7d_tally | Course top: Terrorism + war | 1.5727449 | 0.6655854 | 3 | Kruskal-Wallis rank sum test |
| Q7e_tally | Course top: Water supply | 8.4595963 | 0.0761214 | 4 | Kruskal-Wallis rank sum test |
| Q7f_tally | Course top: Pop. growth | 2.2057888 | 0.6979696 | 4 | Kruskal-Wallis rank sum test |
| Q7g_tally | Course top: Food avail. | 4.9021092 | 0.1791071 | 3 | Kruskal-Wallis rank sum test |
| Q7h_tally | Course top: Disease | 1.5455360 | 0.8185459 | 4 | Kruskal-Wallis rank sum test |
| Q7i_tally | Course top: Poverty | 7.1030371 | 0.0686851 | 3 | Kruskal-Wallis rank sum test |
| Q7j_tally | Course top: Sus. dev. | 16.9109312 | 0.0020115 | 4 | Kruskal-Wallis rank sum test |
| Q7k_tally | Course top: LCA | 4.4471837 | 0.3488562 | 4 | Kruskal-Wallis rank sum test |
| Q7l_tally | Course top: Bio-mimicry | 3.0037680 | 0.3910446 | 3 | Kruskal-Wallis rank sum test |
| Q7m_tally | Course top: Env deg. | 13.9254268 | 0.0075369 | 4 | Kruskal-Wallis rank sum test |
| Q7n_tally | Course top: Culturally app. sol. | 7.2501404 | 0.1232453 | 4 | Kruskal-Wallis rank sum test |
| Q7o_tally | Course top: Opp. for future gen. | 12.2974620 | 0.0152711 | 4 | Kruskal-Wallis rank sum test |
| Q7p_tally | Course top: Female pioneers | 5.5485642 | 0.2354935 | 4 | Kruskal-Wallis rank sum test |
| Q7q_tally | Course top: Under-rep of fem. | 8.9579497 | 0.0621592 | 4 | Kruskal-Wallis rank sum test |
| Q7r_tally | Course top: Under-rep of min. | 7.1316553 | 0.1290923 | 4 | Kruskal-Wallis rank sum test |
| Q7s_tally | Course top: Eng. careers/stages | 10.6811766 | 0.0303908 | 4 | Kruskal-Wallis rank sum test |
| Q7t_tally | Course top: Benefits of being eng. | 9.7037805 | 0.0457243 | 4 | Kruskal-Wallis rank sum test |
| Q7u_tally | Course top: Student stories | 4.3729155 | 0.3578835 | 4 | Kruskal-Wallis rank sum test |
| Q7v_tally | Course top: Teacher stories | 4.6219896 | 0.3283267 | 4 | Kruskal-Wallis rank sum test |
| Q8j | Design course: Concepts to cont. issues | 7.3542516 | 0.1183106 | 4 | Kruskal-Wallis rank sum test |
| Q8n | Design course: Concepts to help ppl | 5.5861029 | 0.2322640 | 4 | Kruskal-Wallis rank sum test |
| Q9a | Sus. minor | 3.4440431 | 0.0634802 | 1 | Kruskal-Wallis rank sum test |
| Q9b | Design for ppl in need | 0.2529607 | 0.6149980 | 1 | Kruskal-Wallis rank sum test |
| Q9c | Design w/ int. ser. | 3.1641257 | 0.0752727 | 1 | Kruskal-Wallis rank sum test |
Q9a - Did you minor in or have a concentration related to sustainability?
## # A tibble: 3 x 2
## Q9a_bin n
## <chr> <int>
## 1 No 2278
## 2 Yes 286
## 3 <NA> 44
##
## No Yes
## 1 936 128
## 2 122 22
## 3 208 31
## 4 232 26
## 5 113 12
## 6 508 54
## 7 159 13
##
## Pearson's Chi-squared test
##
## data: cont_table
## X-squared = 8.2958, df = 6, p-value = 0.2172
Q9b - Did your most recent in-major engineering design project contribut to helping people in need?
## # A tibble: 3 x 2
## Q9b_bin n
## <chr> <int>
## 1 No 1788
## 2 Yes 773
## 3 <NA> 47
##
## No Yes
## 1 729 335
## 2 113 31
## 3 167 72
## 4 180 76
## 5 92 31
## 6 382 180
## 7 125 48
##
## Pearson's Chi-squared test
##
## data: cont_table
## X-squared = 8.8485, df = 6, p-value = 0.1823
Q9c - Did you most recent in-major engineering design course include an international service component?
## # A tibble: 3 x 2
## Q9c_bin n
## <chr> <int>
## 1 No 2316
## 2 Yes 241
## 3 <NA> 51
##
## No Yes
## 1 964 98
## 2 134 9
## 3 218 21
## 4 231 25
## 5 117 6
## 6 499 62
## 7 153 20
##
## Pearson's Chi-squared test
##
## data: cont_table
## X-squared = 7.4818, df = 6, p-value = 0.2786